(3.238.130.97) 您好!臺灣時間:2021/05/18 20:21
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

: 
twitterline
研究生:王琪蕙
研究生(外文):Chi-Hui Wang
論文名稱:報酬波動與交易量的關係-以台灣股票市場為例
論文名稱(外文):Examination of the Return Volatility-Volume Relationships in Taiwan Exchange Stock Market
指導教授:楊踐為楊踐為引用關係
指導教授(外文):Jack J.W. Yang
學位類別:博士
校院名稱:國立雲林科技大學
系所名稱:財務金融系博士班
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:中文
論文頁數:215
中文關鍵詞:Component GARCH模型未預期交易量連續資訊到達假說價量關係波動性
外文關鍵詞:Price-Volume RelationshipVolatilityComponent GARCH ModelUnexpected VolumeSequential Information Arrival Hypothesis (SIAH)
相關次數:
  • 被引用被引用:3
  • 點閱點閱:674
  • 評分評分:
  • 下載下載:264
  • 收藏至我的研究室書目清單書目收藏:0
本文以Component GARCH模型探討臺灣股票市場上市指數報酬波動與交易量的關係,為了討論資訊代理變數的效果將交易量區分為總交易量變動率、預期交易量變動率與未預期交易量變動率,分別探討指數報酬長期(永久性)波動和短期(暫時性)波動與交易量的關係,並以Fama and French(2013)五因子模型(CAPM beta值、市值比、股價淨值比、股價營收比、週轉率)分析最高群組及最低群組的波動影響,本研究主要發現如下:

1、加權指數、電子類、金融保險類的永久性衝擊對長期波動的影響為正向顯著且大於暫時性衝擊對短期波動的負向影響,實施QE2的衝擊影響大於QE1。

2、加權指數、電子類的總交易量變動率對短期波動為正向顯著影響且大於總交易量變動率對長期波動的正向顯著影響,金融保險類的總交易量變動率對長期波動為正向影響且大於總交易量變動率對短期波動的正向影響。

3、加權指數、電子類、市值比重最高群組的預期交易量變動率對長期波動和未預期交易量對短期波動為正向關係,預期交易量對短期波動為負向關係,預期交易量變動率對長期和短期波動的影響大於未預期交易量變動率對長期和短期波動的影響,實施QE2的衝擊影響大於QE1。

4、CGARCH模型加入資訊代理變數,未使得永久性波動的持續性有大幅降低的現象,因此臺灣股票市場較符合SIAH假說。

5、研究期間為全期時,市值比重最低群組、股價營收比最高群組、週轉率最高群組,暫時性衝擊對波動的影響大於永久性衝擊對波動的影響。
This paper uses Component GARCH model to study the stock index return volatility-volume relationships of Taiwan stock market. The trading volume is decomposed into two parts, namely the percentage change of total volume, the percentage change of expected volume and the percentage change of unexpected volume to study the long run (permanent) and short run (transitory) volatility-volume relations. Fama and French (2013) five factors model (CAPM beta, market weighting, P/B, P/EPS, turnover) is used to investigate the impacts of the highest volatility and the lowest volatility groups. The major findings are the following: The long term impacts of TAIEX, Electronics Index, and Finance and Insurance index on the long term volatility are significant and positive. The short term impacts of TAIEX, Electronics Index, and Finance and Insurance index on the short term volatility are significant and negative. The long term positive impacts on the long term volatility are bigger than the short term negative impacts on the short term volatility. The QE2 has greater impact than the QE1. The impacts of TAIEX and Electronics Index trading volume percentage changes on the short term volatility and long term volatility are both significant and positive. The impacts of Finance and Insurance Index trading volume percentage change on the short term volatility and long term volatility are both positive, with greater impact on the long term volatility. The impacts of expected trading volume percentage changes of TAIEX, Electronics Index, and the highest weight of market value group on the long term volatility are positive. The impacts of expected (unexpected) trading volume percentage changes of TAIEX, Electronics Index, and the highest weight of market value group on the short term volatility are negative (positive). The impacts of expected trading volume percentage changes on the short term and long term volatilities are greater than the impacts of the unexpected trading volume percentage changes on the short term and long term volatilities. The impact in QE2 is bigger than the impact in QE1. Including volume in the CGARCH models show that the volatility persistence does not decline deeply. Evidences show that Taiwan stock market satisfies the SIAH. In the whole research period, the transitory component shock is greater than the permanent component shock for the lowest market weight group, the highest price earning ratio group, and the highest turnover rate group.
目錄

中文摘要 i

ABSTRACT ii

誌謝 iv

目錄 v

表目錄 vi

圖目錄 vii

附表目錄 ix

第壹章 緒論 1

第一節 研究背景與動機 1

第二節 研究目的 5

第三節 研究貢獻 6

第四節 文章架構 8

第貳章 文獻回顧 9

第一節 價格和交易量的假說與實證 9

第二節 GARCH模型在價量關係上的研究 12

第三節 Component GARCH模型發展及實證 15

第四節 Component GARCH模型實證小結 19

第參章 研究方法 20

第一節 基本檢定 20

第二節 CGARCH模型設立 36

第三節 CGARCH模型的意涵 41

第四節 建立假說 43

第肆章 實證結果與分析 47

第一節 CGARCH 模型的估計 47

第二節 美國實施第一次量化寬鬆CGARCH 模型的估計 75

第三節 美國實施第二次量化寬鬆CGARCH模型的估計 95

第四節 全期、QE1、QE2的衝擊比率的結果分析 155

第伍章 結論與建議 159

第一節 結論 160

第二節 後續建議 164

參考文獻 165

附錄 174







表目錄

表3-1 指數與交易量的單根檢定 22

表3-2 加權指數與類股指數報酬率、總交易量變動率、未預期交易量變動率敍述統計 24

表3-3 產業別以Fama and French五因子區分的群組統計表 29

表3-4 結構性轉變檢定 34

表4-1 全期CGARCH未加入交易量模型 50

表4-2 全期CGARCH加入總交易量變動率模型 55

表4-3全期CGARCH加入預期交易量變動率和未預期交易量變動率模型 61

表4-4 QE1後期CGARCH未加入交易量模型 78

表4-5 QE1後期CGARCH加入總交易量變動率模型 83

表4-6 QE1後期CGARCH加入預期交易量變動率和未預期交易量變動率模型 89

表4-7 QE2前期CGARCH未加入交易量模型 98

表4-8 QE2前期CGARCH加入總交易量變動率模型 103

表4-9 QE2前期CGARCH加入預期交易量變動率和未預期交易量變動率模型 108

表4-10 QE2後期CGARCH未加入交易量模型 116

表4-11 QE2後期CGARCH加入總交易量變動率模型 121

表4-12 QE2後期CGARCH加入預期交易量變動率和未預期交易量變動率模型 126

表4-13 CGARCH無加量模型以五因子區分最高群組和最低群組彚總表 133

表4-14 CGARCH加入總交易量變動率模型以五因子區分最高群組和最低群組彚總表 140

表4-15 CGARCH加入預期交易量變動率和未預期交易量變動率模型以五因子區分最高群組和最低群組彚總表 146

表4-16 各期間及模型衝擊比率 157























圖目錄

圖4-1 加權指數CGARCH模型條件變異數、長期波動、短期波動趨勢分解圖 67

圖4-2 水泥類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 67

圖4-3 食品類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 68

圖4-4 塑膠類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 68

圖4-5 紡織纖維類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 68

圖4-6 電機機械類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 68

圖4-7 電器電纜類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 69

圖4-8 化學生技醫療類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 69

圖4-9 化學類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 69

圖4-10 生技醫療類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 69

圖4-11 玻璃陶瓷類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 70

圖4-12 造紙類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 70

圖4-13 鋼鐵類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 70

圖4-14 橡膠類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 70

圖4-15 汽車類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 71

圖4-16 電子類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 71

圖4-17 半導體類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 71

圖4-18 電腦及週邊設備類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 71

圖4-19 光電類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 72

圖4-20 通信網路類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 72

圖4-21 電子零組件類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 72

圖4-22 電子通路類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 72

圖4-23 資訊服務類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 73

圖4-24 其他電子業類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 73

圖4-25 營造建材類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 73

圖4-26 航運類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 73

圖4-27 觀光類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 74

圖4-28 金融保險類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 74

圖4-29 百貨貿易類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 74

圖4-30 油電燃氣類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 74

圖4-31 其他類CGARCH 模型條件變異數、長期波動、短期波動趨勢分解圖 75

















































附表目錄

附表1 QE1前期CGARCH未加入交易量模型 174

附表2 QE1前期CGARCH加入總交易量變動率模型 177

附表3 QE1前期CGARCH加入預期交易量變動率和未預期交易量變動率模型 180

附表4 QE1中期CGARCH未加入交易量模型 185

附表5 QE1中期CGARCH加入總交易量變動率模型 188

附表6 QE1中期CGARCH加入預期交易量變動率和未預期交易量變動率模型 191

附表7 QE2中期CGARCH未加入交易量模型 195

附表8 QE2中期CGARCH加入總交易量變動率模型 198

附表9 QE2中期CGARCH加入預期交易量變動率和未預期交易量變動率模型 202
陳家彬,1999,“台灣地區股票報酬之橫斷面分析:三因子模式之實證”, 興大人文社會學報,8, 213-236.

楊奕農,2009,時間序列分析經濟與財務上之應用,二版,雙葉書廊有限公司,臺北市。

Adrian, T. and Rosenberg, J. (2008), “Stock returns and volatility: pricing the short-run and long-run components of market risk”, Journal of Finance, 63 (6), 2997-3030.

Andersen T. G. and Bollerslev, T. (1997a), “Intra-day periodicity and volatility persistence in financial markets”, Journal of Empirical Finance, 4, 115–158.

Andersen T. G. and Bollerslev, T. (1997b), “Heterogeneous information arrivals and return volatility dynamics: uncovering the long-run in high frequency returns”, Journal of Finance, 52, 975–1005.

Anderson, T. G. (1996), “Return volatility and trading volume: an information flow interpretation of stochastic volatility”, Journal of Finance, 51, 169-204.

Ane, T. (2006), “ Short and long term components of volatility in Hong Kong stock returns”, Applied Financial Economics, 16 (6), 439-460.

Arago, V. and Nieto, L. (2005), “Heteroskedasticity in the returns of the main world stock exchange indices: volume versus GARCH effects”, International Financial Markets, Institutions & Money, 15, 271-284.

Asteriou, D. and Hall, S. G. (2007), Applied Econometrics: A Modern Approach Using Eviews and Microfit, Revised Edition, Hampshire: Palgrave Macmillan.

Bessembinder, H. and Seguin, P. J. (1993), “Price volatility, trading volume, and market depth: evidence from futures markets”, Journal of Financial and Quantitative Analysis, 28, 21 – 39.

Bollerslev, T. (1986), “Generalized autoregressive conditional heteroskedasticity”, Journal of Econometrics, 31, 307–327.

Bollerslev, T. and Zhou, H. (2002), “Estimating stochastic volatility diffusion using conditional moments of integrated volatility”, Journal of Econometrics, 109, 33-65.

Bollerslev, T., Chou, R. Y., and Kroner, K. F. (1992), “ARCH modeling in finance: a review of the theory and empirical evidence”, Journal of Econometrics, 52, 5–59.

Bollerslev, T., Engle, R. F., and Nelson, D. B. (1994), “ARCH Models,” in Handbook of Econometrics, eds. by Engle, R. F., and McFadden, D. L., vol. 4, 2959–3038. Elsevier Science, Amsterdam: North-Holland.

Bollerslev, T., Engle, R.F., and Nelson, D. B. (1993), “ARCH Models” , in Handbook of Econometrics, eds. by Griliches, Z., and Intriligator, M.D., vol.2, 2963-2965, Amsterdam: North-Holland.

Box, G. E. P. and Jenkins, G. (1976), Time Series Analysis: Forecasting and Control, San Francisco: Holden-Day.

Brooks, C. (2002), Introductory Econometrics for Finance, Cambridge University Press, USA.

Byrne, J. P. and Davis, E. P. (2005), “The impact of short- and long-run exchange rate uncertainty on investment: a panel study of industrial countries”, Oxford bulletin of economics and statistics, 67 (3), 307-329.

Campbell, J. Y., Grossman, S. J., and Wang, J. (1993), “Trading volume and serial correlation in stock returns”, Quarterly Journal of Economics, 108, 905-939.

Chan, L. K., Jegadee, N., and Lakonishok, J. (1999), “The profitability of momentum strategies”, Journal of Banking and Finance, 23, 897-924.

Chen, Z. and Daigler, R. T. (2008), “An examination of the complementary volume-volatility information theories”, Journal of Futures Markets, 28, 963-992.

Chernov, M., Gallant, A. R., Ghysels, E. and Tauchen, G. (2003), “Alternative models for stock price dynamics”, Journal of Econometrics, 116, 225-257.

Cho, J. H. and Ahmed, F. E. (2011), “Predicting time-varying long-run variance- modified component GARCH model approach”, Journal of Financial and Economics Practice, 11 (1), 52-68.

Christoffersen, P., Jacobs, K., Ornthanalai, C., and Wang, Y. (2008), “Option valuation with long-run and short-run volatility components”, Journal of Financial Economics, 90, 272-297.

Clark, P. K. (1973), “A subordinated stochastic process model with finite variance for speculative prices”, Econometrica, 41, 135-155.

Copeland, T. E. (1976), “A model of asset trading under the assumption of sequential information arrival”, Journal of Finance, 31, 1149-1168.

Ding, Z. and Granger, C. W. J. (1996) “Modeling volatility persistence of speculative returns: a new approach”, Journal of Econometrics, 73, 185–215.

Engle, R. F. (2001), “GARCH101: the use of ARCH/GARCH models in applied econometrics”, Journal of Economic Perspectives, 15, 157-168.

Engle, R. F. and Lee, G. G. J. (1999), “A long–run and short–run component model of stock return volatility”, in Cointegration, Causality, and Forecasting, eds. by Engle, R. F., and White, H., pp. 475–497, Oxford University Press, New York.

Engle, R.F. (1982), “ Autoregressive conditional heteroscedasticity with estimate of the variance of United Kingdom inflation”, Econometrica, 50, 987-1007.

Epps, T. W. (1976), “The demand for brokers’ services: the relation between security trading volume and transaction cost”, Bell Journal of Economics, 7, 163-194.

Epps, T.W. and Epps, M. L. (1976), “The stochastic dependence of security price changes and transaction volumes: implications for the mixture of distributions hypothesis”, Econometric, 44, 305-321.

Fama, E. F. (1965), “The behavior of stock market prices”, Journal of Business, 38, 383-417.

Fama, E. F. and French, K. R. (1993), “Common risk factors in the returns on stocks and bonds”, Journal of Financial Economics, 33, 1, 3–56.

Fama, E. F. and French, K. R. (2013), “A five-factor asset pricing model”, Fama-Miller Working Paper. Available at SSRN: http://ssrn.com/abstract=2287202 or http://dx.doi.org/10.2139/ssrn.2287202

Floros, C. (2007), “The use of GARCH models for the calculation of minimum capital risk requirements: international evidence”, International Journal of Managerial Finance, 3 (4), 360-371.

Floros, C. (2008), “Modelling volatility using GARCH models: evidence from Egypt and Israel”, Middle Eastern Finance and Economics, 2, 31-41.

Gallagher, L. (1999), “A multi-country analysis of the temporary and permanent components of stock prices”, Applied Financial Economics, 9, 129–142.

Gallant, A. R., Rossi, P. E., and Tauchen, G. (1992), “Stock prices and volume”, Review of Financial Studies, 5, 199−242.

Ghysels, E., Santa-Clara, P., and Valkanov, R. (2005), “There is a risk-return tradeoff after all”, Journal of Financial Economics, 76, 509–548.

Gilbert C. L. (2010), “How to understand high food prices”, Journal of Agricultural Economics, 61 (2), 398-425.

Girma, P. B. and Mougoue, M. (2002), “An empirical examination of the relation between futures spreads volatility, volume, and open interest”, Journal of Futures Markets, 22, 1083-1102.

Granger, C. and Newbold, P. (1974), “Spurious regressions in econometrics”, Journal of Econometrics, 2, 111-120.

Guo, H. and Neely, C. J. (2008), “ Investigating the intertemporal risk-return relation in international stock markets with the component GARCH model”, Economics Letters, 99, 371-374.

Haas, M. (2007), “Volatility components and long memory-effects revisited”, Studies in Nonlinear Dynamics & Econometrics, 11 (2), Article 3.

Hammoudeh, S. and Yuan, Y. (2008), “Metal volatility in presence of oil and interest rate shocks”, Energy Economics, 30, 606–620.

Harris, M. and Raviv, A. (1993), “Differences of opinion make a horse race”, Review of Financial Studies, 6, 473-506.

Hua, M. and Gau, Y. F. (2007), “Intraday exchange rate volatility: ARCH, news and seasonality effects”, Quarterly Review of Economics & Finance, 47, 135-158.

Huang, C. M., Lin, T. Y., Yu, C. H., and Hoe, S. Y. (2006), “Volatility-volume relationships among types of traders considering the investment limitation to foreign investors”, Review of Pacific Basin Financial Markets & Policies, 9, 575-596

Hwang, S. and Satchell, S. E. (2000), “Market risk and the concept of fundamental volatility: measuring volatility across asset and derivative markets and testing for the impact of derivatives markets on financial markets”, Journal of Banking and Finance, 24 (4), 759-785.

Hwang, S. and Satchell, S. E. (2005), “ GARCH model with cross-sectional volatility: GARCH models”, Applied Financial Economics, 15, 203–216.

James, C. and Edmister, R. (1983), “ The relation between common stock returns, trading activity, and market value”, Journal of Finance, 38, 1075-1086.

Jennings, R. H., Starks, L. T., and Fellingham, J. C. (1981), “An equilibrium model of asset trading with sequential information arrival”, Journal of Finance, 19, 143-161.

Kang, S. H., Kang, S .M. and Yoon, S. M. (2009), “ Forecasting volatility of crude oil markets”, Energy Economics, 31 (1), 119-125.

Karpoff, J. M. (1986), “A theory of trading volume”, Journal of Finance, 41 (5), 1069-1087.

Kearney, C. and Daly, K. (1998), “The causes of stock market volatility in Australia”, Applied Financial Economics, 8, 597-605.

Kim, D. and Kon, S. I. (1994), “Alternative models for conditional heteroscedasticity of stock returns”, Journal of Business, 67, 563-598.

Kiymaz, H. and Girard, E. (2009), “Stock market volatility and trading volume: an emerging market experience”, The Icfai Journal of Applied Finance, 15 (6), 6-32

Kontonikas, A. (2004), “Inflation and inflation uncertainty in the United Kingdom, evidence from GARCH modeling”, Economic Modelling, 21, 525-543.

Lamoureux, C. G. and Lastrapes, W. D. (1990), “Heteroskedasticity in stock return data: volume versus GARCH effects”, Journal of Finance, 45, 221-229.

Lee, B. S. and Rui, O. M. (2002), “The dynamic relationship between stock returns and trading volume”, Journal of Banking and Finance, 26, 51-78.

Lee, C. H., Lin, S. H., and Liu, Y. C. (2010), “Volatility and trading activity: an application of the component-GARCH model”, The Empirical Economics Letters, 9 (1), 99-106.

Lee, C. L. and Reed, R. (2011), “Volatility decomposition of Australian housing prices”, The 17th Pacific Rim Real Estate Society Conference, Gold Coast, Australia, 16th-19th January.

Liow, K. H. and Ibrahim, M. F. (2010), “Volatility decomposition and correlation in international securitized real estate markets”, Journal of Real Estate Finance and Economics, 40, 221-243.

Maheu, J. (2005), “Can GARCH models capture long-range dependence?”, Studies in Nonlinear Dynamics & Econometrics, 9 (4), 1-41.

Maheu, J. M. and Mccurdy, T. H. (2007), “Components of market risk and return”, Journal of Financial Econometrics, 5 (4), 560-590.

Merton, R. (1973), “An intertemporal capital asset pricing model”, Econometrica, 41, 867-887.

Morgan, I. G. (1976), “Stock prices and heteroscedasticity”, Journal of Business, 49, 407-422.

Nelson, D. B. (1991), “Conditional heteroskedasticity in asset returns: a new approach”, Econometrica, 59 (1), 347-370.

Nieto, L., Fernandez, A., and Munoz, M. J. (2001), “Unexpected volume modeling in the Ibex-35 futures market”, Derivatives Use Trading & Regulation, 1, 37-54.

Pagan, A. R. and Schwert, G. W. (1990), “Alternative models for conditional stock volatility”, Journal of Econometrics , 45, 264-290.

Palm, F. C. (1996), “GARCH models of volatility” , in Handbook of Statistics, eds. by Maddala, G. S., and Rao, C. R., vol.14, pp. 209–240. Amsterdam: Elsevier Sciences.

Ragunathan, V. and Peker, A. (1997), “Price variability, trading volume and market depth: evidence from the Australian futures market”, Applied Financial Economics, 7, 447-454.

Roll, R. (1984), “A simple implicit measure of the effective bid-ask spread in an efficient market”, Journal of Finance, 39 (4), 1127-1139.

Said, E. S. and Dickey, D. A. (1994), “Testing for unit roots in autoregressive-moving average models of unknown order”, Biometrica, 71, 599-607.

Sentana, E. and Wadhwani, S. (1992), “Feedback traders and stock return autocorrelations: evidence from a century of daily data”, Economic Journal, 102, 415-425.

Sharma, J. L., Mougoue, M., and Kamath, R. (1996), “Heteroscedasticity in stock market indicator return data: volume versus GARCH effects”, Applied Financial Economics , 6, 337–342.

Shephard, N. (1996), “Statistical aspects of ARCH and stochastic volatility”, Monographs on Statistics and Applied Probability, 65, 1-68.

Skinner, D. (1989), “Option markets and stock return volatility”, Journal of Financial Economics, 23, 61-78.

Smirlock, M. and Starks, L. (1988), “An empirical analysis of the stock price-volume relationship”, Journal of Banking and Finance, 12, 31-41.

Tauchen, G. and Pitts, M. (1983), “The price variability-volume relationship on speculative markets.”, Econometrica, 51, 485-505.

Tsay, R. S. (2002), Analysis of Financial Time Series, Wiley Series in Probability and Statistics.

Wang, J. (1994), “A model of competitive stock trading volume”, Journal of Political Economy, 102, 127-168.

Wei, C. C. (2009), “Using the component GARCH modeling and forecasting Method to determine the effect of unexpected exchange rate mean and volatility spillover on stock markets”, International Research Journal of Finance and Economics, 23, 62-74.

Wen, F. and Yang, X. (2009), “Empirical study on relationship between persistence-free trading volume and stock return volatility”, Global Finance Journal, 20, 119-127.

Yamani, E. (2011), “The informational efficiency of the corporate bond market: What is the role of trading volume?”, Journal of Applied Financial Research, 1, 9-37.

Yang, J. J. W. and You, S. J. (2003), “Asymmetric volatility: pre and post Asian financial crisis”, Journal of Management, 20, 797-819.

Ying, C. C. (1966), “Stock market prices and volumes of sales”, Econometrica, 34, 676-686.

Zarour, B. A. and Siriopoulos, C. P. (2008), “Transitory and permanent volatility components: the case of the middle east stock markets”, Review of Middle East Economics and Finance, 4 (2), Article 3.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top